import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard

		from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline




@require_flax
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							with tempfile.TemporaryDirectory() as tmpdirname:
											# pipeline has Flax weights
											__lowercase					     =				FlaxDiffusionPipeline.from_pretrained(
											    '''hf-internal-testing/tiny-stable-diffusion-pipe''' ,  safety_checker=lowerCAmelCase__ ,  cache_dir=lowerCAmelCase__		)

											__lowercase					     =				[t[-1] for t in os.walk(os.path.join(lowerCAmelCase__ ,  os.listdir(lowerCAmelCase__		)[0] ,  '''snapshots'''		)		)]
											__lowercase					     =				[item for sublist in all_root_files for item in sublist]

											# None of the downloaded files should be a PyTorch file even if we have some here:
											# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
											assert not any(f.endswith('''.bin'''		) for f in files		)




@slow
@require_flax
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				FlaxStableDiffusionPipeline.from_pretrained(
							    '''hf-internal-testing/tiny-stable-diffusion-pipe''' ,  safety_checker=lowerCAmelCase__		)

							__lowercase					     =				(
							    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
							    ''' field, close up, split lighting, cinematic'''
							)

							__lowercase					     =				jax.random.PRNGKey(0		)
							__lowercase					     =				4

							__lowercase					     =				jax.device_count()
							__lowercase					     =				num_samples * [prompt]
							__lowercase					     =				pipeline.prepare_inputs(lowerCAmelCase__		)

							# shard inputs and rng
							__lowercase					     =				replicate(lowerCAmelCase__		)
							__lowercase					     =				jax.random.split(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				shard(lowerCAmelCase__		)

							__lowercase					     =				pipeline(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  jit=lowerCAmelCase__		).images

							assert images.shape == (num_samples, 1, 64, 64, 3)
							if jax.device_count() == 8:
											assert np.abs(np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa		).sum() - 4.151_4745		) < 1E-3
											assert np.abs(np.abs(lowerCAmelCase__ ,  dtype=np.floataa		).sum() - 4_9947.875		) < 5E-1

							__lowercase					     =				pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
							assert len(lowerCAmelCase__		) == num_samples





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				FlaxStableDiffusionPipeline.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4''' ,  revision='''flax''' ,  safety_checker=lowerCAmelCase__		)

							__lowercase					     =				(
							    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
							    ''' field, close up, split lighting, cinematic'''
							)

							__lowercase					     =				jax.random.PRNGKey(0		)
							__lowercase					     =				50

							__lowercase					     =				jax.device_count()
							__lowercase					     =				num_samples * [prompt]
							__lowercase					     =				pipeline.prepare_inputs(lowerCAmelCase__		)

							# shard inputs and rng
							__lowercase					     =				replicate(lowerCAmelCase__		)
							__lowercase					     =				jax.random.split(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				shard(lowerCAmelCase__		)

							__lowercase					     =				pipeline(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  jit=lowerCAmelCase__		).images

							assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
							if jax.device_count() == 8:
											assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa		).sum() - 0.0565_2401)		) < 1E-3
											assert np.abs((np.abs(lowerCAmelCase__ ,  dtype=np.floataa		).sum() - 238_3808.2)		) < 5E-1





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				FlaxStableDiffusionPipeline.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4''' ,  revision='''bf16''' ,  dtype=jnp.bfloataa ,  safety_checker=lowerCAmelCase__		)

							__lowercase					     =				(
							    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
							    ''' field, close up, split lighting, cinematic'''
							)

							__lowercase					     =				jax.random.PRNGKey(0		)
							__lowercase					     =				50

							__lowercase					     =				jax.device_count()
							__lowercase					     =				num_samples * [prompt]
							__lowercase					     =				pipeline.prepare_inputs(lowerCAmelCase__		)

							# shard inputs and rng
							__lowercase					     =				replicate(lowerCAmelCase__		)
							__lowercase					     =				jax.random.split(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				shard(lowerCAmelCase__		)

							__lowercase					     =				pipeline(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  jit=lowerCAmelCase__		).images

							assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
							if jax.device_count() == 8:
											assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa		).sum() - 0.0400_3906)		) < 1E-3
											assert np.abs((np.abs(lowerCAmelCase__ ,  dtype=np.floataa		).sum() - 237_3516.75)		) < 5E-1





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				FlaxStableDiffusionPipeline.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4''' ,  revision='''bf16''' ,  dtype=jnp.bfloataa		)

							__lowercase					     =				(
							    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
							    ''' field, close up, split lighting, cinematic'''
							)

							__lowercase					     =				jax.random.PRNGKey(0		)
							__lowercase					     =				50

							__lowercase					     =				jax.device_count()
							__lowercase					     =				num_samples * [prompt]
							__lowercase					     =				pipeline.prepare_inputs(lowerCAmelCase__		)

							# shard inputs and rng
							__lowercase					     =				replicate(lowerCAmelCase__		)
							__lowercase					     =				jax.random.split(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				shard(lowerCAmelCase__		)

							__lowercase					     =				pipeline(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  jit=lowerCAmelCase__		).images

							assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
							if jax.device_count() == 8:
											assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa		).sum() - 0.0400_3906)		) < 1E-3
											assert np.abs((np.abs(lowerCAmelCase__ ,  dtype=np.floataa		).sum() - 237_3516.75)		) < 5E-1





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				FlaxDDIMScheduler(
							    beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  set_alpha_to_one=lowerCAmelCase__ ,  steps_offset=1 ,  )

							__lowercase     ,	__lowercase					     =				FlaxStableDiffusionPipeline.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4''' ,  revision='''bf16''' ,  dtype=jnp.bfloataa ,  scheduler=lowerCAmelCase__ ,  safety_checker=lowerCAmelCase__ ,  )
							__lowercase					     =				scheduler.create_state()

							__lowercase					     =				scheduler_state

							__lowercase					     =				(
							    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
							    ''' field, close up, split lighting, cinematic'''
							)

							__lowercase					     =				jax.random.PRNGKey(0		)
							__lowercase					     =				50

							__lowercase					     =				jax.device_count()
							__lowercase					     =				num_samples * [prompt]
							__lowercase					     =				pipeline.prepare_inputs(lowerCAmelCase__		)

							# shard inputs and rng
							__lowercase					     =				replicate(lowerCAmelCase__		)
							__lowercase					     =				jax.random.split(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				shard(lowerCAmelCase__		)

							__lowercase					     =				pipeline(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  jit=lowerCAmelCase__		).images

							assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
							if jax.device_count() == 8:
											assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa		).sum() - 0.0_4504_3945)		) < 1E-3
											assert np.abs((np.abs(lowerCAmelCase__ ,  dtype=np.floataa		).sum() - 234_7693.5)		) < 5E-1





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				(
							    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
							    ''' field, close up, split lighting, cinematic'''
							)

							__lowercase					     =				jax.device_count()
							__lowercase					     =				num_samples * [prompt]
							__lowercase					     =				jax.random.split(jax.random.PRNGKey(0		) ,  lowerCAmelCase__		)

							__lowercase     ,	__lowercase					     =				FlaxStableDiffusionPipeline.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4''' ,  revision='''bf16''' ,  dtype=jnp.bfloataa ,  safety_checker=lowerCAmelCase__ ,  )

							__lowercase					     =				replicate(lowerCAmelCase__		)
							__lowercase					     =				pipeline.prepare_inputs(lowerCAmelCase__		)
							__lowercase					     =				shard(lowerCAmelCase__		)
							__lowercase					     =				pipeline(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  jit=lowerCAmelCase__		).images
							assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
							__lowercase					     =				images[2, 0, 2_56, 10:17, 1]

							# With memory efficient attention
							__lowercase     ,	__lowercase					     =				FlaxStableDiffusionPipeline.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4''' ,  revision='''bf16''' ,  dtype=jnp.bfloataa ,  safety_checker=lowerCAmelCase__ ,  use_memory_efficient_attention=lowerCAmelCase__ ,  )

							__lowercase					     =				replicate(lowerCAmelCase__		)
							__lowercase					     =				pipeline.prepare_inputs(lowerCAmelCase__		)
							__lowercase					     =				shard(lowerCAmelCase__		)
							__lowercase					     =				pipeline(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  jit=lowerCAmelCase__		).images
							assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
							__lowercase					     =				images[2, 0, 2_56, 10:17, 1]

							# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
							# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
							assert abs(slice_eff - slice		).max() < 1E-2
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	List[Any]         =    logging.get_logger(__name__)

__a    :	List[Any]         =    {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Optional[Any]     					=		'''audio-spectrogram-transformer'''





			def __init__(  self ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-12 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=1_28 ,  **lowerCAmelCase__ ,  )	->   Optional[Any]:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__		)

							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				initializer_range
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				patch_size
							__lowercase					     =				qkv_bias
							__lowercase					     =				frequency_stride
							__lowercase					     =				time_stride
							__lowercase					     =				max_length
							__lowercase					     =				num_mel_bins